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How would you handle the vanishing gradient problem and exploding gradient problem?

Data Scientist

Amazon

AppDynamics

NetSuite

Confluent

Zillow

WeWork

How would you handle the vanishing gradient problem and exploding gradient problem?
It's a well-known issue that gradient problems can hamper deep learning productivity. How do you usually handle them?
Gradient issues can be frustrating in deep learning. What are your tried-and-tested techniques to manage these problems?
As a machine learning expert, can you share some of your insights on how to deal with gradient problems, particularly the vanishing and exploding type?
How do you approach gradient issues in deep learning, particularly the vanishing and exploding problems?
As a practitioner in deep learning, what steps do you typically take to overcome vanishing and exploding problems in gradient descent?
When faced with gradient problems, how do you modify your deep learning algorithms to ensure stable and accurate predictions?
Gradient issues can be a major roadblock in deep learning. Can you describe your process of mitigating these problems?
What steps do you take to deal with gradient problems in deep learning, and how do you evaluate their effectiveness?
What are your strategies to tackle the vanishing or exploding gradient problem in deep learning?

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Interview question asked to Data Scientists interviewing at WeWork, NetSuite, NetApp and others: How would you handle the vanishing gradient problem and exploding gradient problem?.

How would you handle the vanishing gradient problem and exploding gradient problem?

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